html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2242#issuecomment-453866106,https://api.github.com/repos/pydata/xarray/issues/2242,453866106,MDEyOklzc3VlQ29tbWVudDQ1Mzg2NjEwNg==,2443309,2019-01-13T21:13:28Z,2019-01-13T21:13:28Z,MEMBER,I just reran the example above and things seem to be resolved now. The write step for the two datasets is basically identical. ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,334633212
https://github.com/pydata/xarray/issues/2242#issuecomment-399503156,https://api.github.com/repos/pydata/xarray/issues/2242,399503156,MDEyOklzc3VlQ29tbWVudDM5OTUwMzE1Ng==,1217238,2018-06-22T16:33:11Z,2018-06-22T16:33:11Z,MEMBER,"This autoclose business is really hard to reason about in its current version, as part of the backend class. I'm hoping that refactoring it out into a separate object that we can use with composition instead of inheritance will help (e.g., alongside [PickleByReconstructionWrapper](https://github.com/pydata/xarray/blob/73b476e4db6631b2203954dd5b138cb650e4fb8c/xarray/backends/common.py#L513)).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,334633212
https://github.com/pydata/xarray/issues/2242#issuecomment-399320127,https://api.github.com/repos/pydata/xarray/issues/2242,399320127,MDEyOklzc3VlQ29tbWVudDM5OTMyMDEyNw==,2443309,2018-06-22T04:51:54Z,2018-06-22T04:51:54Z,MEMBER,"I think, at least to some extent, the performance hit is to be expected. I don't think we should be opening the file more than once when using the serial or threaded schedulers so that may be a place where you can find some improvement. There will always be a performance hit when writing dask arrays to netcdf files chunk-by-chunk. For 1, there is a threading lock that limits parallel throughput. More importantly, the chunked writes are going to always be slower than larger reads coming directly from numpy arrays. 

In your example above, the snippit @shoyer mentions should evaluate to `autoclose=False`. However, the profiling you mention seems to indicate the opposite. Perhaps we should start by digging deeper on that point. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,334633212
https://github.com/pydata/xarray/issues/2242#issuecomment-399275847,https://api.github.com/repos/pydata/xarray/issues/2242,399275847,MDEyOklzc3VlQ29tbWVudDM5OTI3NTg0Nw==,1217238,2018-06-21T23:37:10Z,2018-06-21T23:37:10Z,MEMBER,"I suspect this can be improved. Looking at the code, it appears that we only intentionally use `autoclose=True` for writes when using multiprocessing or the distributed dask scheduler.
https://github.com/pydata/xarray/blob/73b476e4db6631b2203954dd5b138cb650e4fb8c/xarray/backends/api.py#L709-L710","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,334633212